app.py

import gradio as gr
from transformers import pipeline

# Load the pre-trained NER model
model = pipeline("ner", model="/home/user/app/mendobert/", tokenizer="indolem/indobert-base-uncased")
# basemodel = pipeline("ner", model="/home/user/app/base-model/", tokenizer="indolem/indobert-base-uncased")

def text_analysis(text):
    doc = model(text)
    html = displacy.render(doc, style="dep", page=True)
    html = (
        "<div style='max-width:100%; max-height:360px; overflow:auto'>"
        + html
        + "</div>"
    )
    pos_count = {
        "char_count": len(text),
        "token_count": 0,
    }
    pos_tokens = []

    for token in doc:
        pos_tokens.extend([(token.text, token.pos_), (" ", None)])

    return pos_tokens, pos_count, html

demo = gr.Interface(
    text_analysis,
    gr.Textbox(placeholder="Enter sentence here..."),
    ["highlight", "json", "html"],
    examples=[
        ["Aspartylglucosaminuria (AGU) adalah gangguan metabolisme glikoprotein langka."],
        ["Mutasi germ - line dari gen BRCA1 membuat wanita cenderung mengalami kanker payudara dini dengan mengorbankan fungsi presumtif gen sebagai penekan tumor."],
    ],
)

demo.launch()